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Year 2015, Volume: 64 Issue: 2, 89 - 98, 01.08.2015
https://doi.org/10.1501/Commua1_0000000736

Abstract

References

  • K. Xiong, H. Zhang and C.W. Chan, “Performance Evaluation UKF- Based Nonlinear Filtering”, Automatica, Elsevier, 42 (2), pp. 261-270, 2006.
  • K.H. Kim, J.G. Lee, C.G. Park and G.I. Jee, “The Stability Analysis of the Adaptive Fading Kalman Filter”, 16 th IEEE International Conference on Control Applications, IEEE, Singapore, pp. 982-987, 1-3 October 2007. 140 160 200 20 40 60 80 100 k 200
  • S.J. Julier, J.K. Uhlmann and H.F. Durratnt-Whyte, “A new Approach for Filtering Nonlinear system”. Proceedings of American Control Conference, pp. 1628-1632, Washington, DC, 1995.
  • S.J. Julier and J.K. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear Systems”, Int. Symp. Aerospace/Defense Sensing, Simul. and Controls, Orlando, FL, SPIE, doi: 10.1117/12.280797, pp. 182-193, 21–24 April 1997.
  • S.J. Julier and J. Uhlmann, “The Scaled Unscented Transformation”, American Control Conference, IEEE, Anchorage, pp. 4555-4559, 2002.
  • B. Ristic, A. Farina, D. Benvenuti and M.S. Arulampalam. “Performance Bounds and Comparision of Nonlinear Filters for Tracking a Ballistic Object on Re-entry”, IEEE proceedings of the radar Sonar Navigation, 150 (2), pp. 65-70, 2003.
  • Wan, E. A. and Van der Merwe, R. (2000). “The Unscented Kalman Filter for Nonlinear Estimation”,. Adaptive Systems for signal processing, Communications and Control Symposium, IEEE, pp. 153-158, 1-4 October 2000.
  • C. Chui, G. Chen and H.C. Chui, “Modified Extended Kalman Filtering and a Real-Time Parallel Algorithm for System Parameter Identification”, IEEE, Transactions on Automatic Control, 35 (1), pp. 100-104, January 1990.
  • K. Xiong, L.D. Liu and H.Y. Zhang. “Modified Unscented Kalman Filtering and its Application in Autonomous Satellite Navigation”, Aerospace Science and Tecnology, Elsevier, 13 (4), pp. 238-246, 2009.
  • Current Address: Esin Köksal Babacan, Ankara University, Department of Statistics, Faculty of Science, 06100 Tandoğan, Ankara-TURKEY

MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS

Year 2015, Volume: 64 Issue: 2, 89 - 98, 01.08.2015
https://doi.org/10.1501/Commua1_0000000736

Abstract

The Extended Kalman Filter (EKF) is the often used filtering algorithm for nonlinear systems. But it does not usually produce desirable results. Recently a new nonlinear filtering algorithm named as Unscented Kalman Filter (UKF) is introduced. In this paper, we propose a new modified Unscented Kalman Filter (MUKF) algorithm for nonlinear stochastic systems that are linear in some components. These nonlinear systems can be considered as having linear subsystems with parameters and aim is to estimate the system parameters. In simulation study, performance of the EKF, its known variant Modified Extended Kalman Filter (MEKF), UKF and the proposed MUKF is demonstrated for a nonlinear system that is linear in some components. The results show that MUKF gives the best solution for parameter identification problem

References

  • K. Xiong, H. Zhang and C.W. Chan, “Performance Evaluation UKF- Based Nonlinear Filtering”, Automatica, Elsevier, 42 (2), pp. 261-270, 2006.
  • K.H. Kim, J.G. Lee, C.G. Park and G.I. Jee, “The Stability Analysis of the Adaptive Fading Kalman Filter”, 16 th IEEE International Conference on Control Applications, IEEE, Singapore, pp. 982-987, 1-3 October 2007. 140 160 200 20 40 60 80 100 k 200
  • S.J. Julier, J.K. Uhlmann and H.F. Durratnt-Whyte, “A new Approach for Filtering Nonlinear system”. Proceedings of American Control Conference, pp. 1628-1632, Washington, DC, 1995.
  • S.J. Julier and J.K. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear Systems”, Int. Symp. Aerospace/Defense Sensing, Simul. and Controls, Orlando, FL, SPIE, doi: 10.1117/12.280797, pp. 182-193, 21–24 April 1997.
  • S.J. Julier and J. Uhlmann, “The Scaled Unscented Transformation”, American Control Conference, IEEE, Anchorage, pp. 4555-4559, 2002.
  • B. Ristic, A. Farina, D. Benvenuti and M.S. Arulampalam. “Performance Bounds and Comparision of Nonlinear Filters for Tracking a Ballistic Object on Re-entry”, IEEE proceedings of the radar Sonar Navigation, 150 (2), pp. 65-70, 2003.
  • Wan, E. A. and Van der Merwe, R. (2000). “The Unscented Kalman Filter for Nonlinear Estimation”,. Adaptive Systems for signal processing, Communications and Control Symposium, IEEE, pp. 153-158, 1-4 October 2000.
  • C. Chui, G. Chen and H.C. Chui, “Modified Extended Kalman Filtering and a Real-Time Parallel Algorithm for System Parameter Identification”, IEEE, Transactions on Automatic Control, 35 (1), pp. 100-104, January 1990.
  • K. Xiong, L.D. Liu and H.Y. Zhang. “Modified Unscented Kalman Filtering and its Application in Autonomous Satellite Navigation”, Aerospace Science and Tecnology, Elsevier, 13 (4), pp. 238-246, 2009.
  • Current Address: Esin Köksal Babacan, Ankara University, Department of Statistics, Faculty of Science, 06100 Tandoğan, Ankara-TURKEY
There are 10 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Esin Babacan Koksal This is me

İ. Doroslovackı Milos This is me

Levent Özbek This is me

Publication Date August 1, 2015
Published in Issue Year 2015 Volume: 64 Issue: 2

Cite

APA Babacan Koksal, E., Doroslovackı Milos, İ., & Özbek, L. (2015). MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 64(2), 89-98. https://doi.org/10.1501/Commua1_0000000736
AMA Babacan Koksal E, Doroslovackı Milos İ, Özbek L. MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. August 2015;64(2):89-98. doi:10.1501/Commua1_0000000736
Chicago Babacan Koksal, Esin, İ. Doroslovackı Milos, and Levent Özbek. “MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 64, no. 2 (August 2015): 89-98. https://doi.org/10.1501/Commua1_0000000736.
EndNote Babacan Koksal E, Doroslovackı Milos İ, Özbek L (August 1, 2015) MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 64 2 89–98.
IEEE E. Babacan Koksal, İ. Doroslovackı Milos, and L. Özbek, “MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 64, no. 2, pp. 89–98, 2015, doi: 10.1501/Commua1_0000000736.
ISNAD Babacan Koksal, Esin et al. “MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 64/2 (August 2015), 89-98. https://doi.org/10.1501/Commua1_0000000736.
JAMA Babacan Koksal E, Doroslovackı Milos İ, Özbek L. MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2015;64:89–98.
MLA Babacan Koksal, Esin et al. “MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 64, no. 2, 2015, pp. 89-98, doi:10.1501/Commua1_0000000736.
Vancouver Babacan Koksal E, Doroslovackı Milos İ, Özbek L. MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2015;64(2):89-98.

Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.

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